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arXiv · 2206.04607

On Margins and Generalisation for Voting Classifiers

Felix Biggs·Valentina Zantedeschi·Benjamin Guedj
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Abstract

We study the generalisation properties of majority voting on finite ensembles of classifiers, proving margin-based generalisation bounds via the PAC-Bayes theory. These provide state-of-the-art guarantees on a number of classification tasks. Our central results leverage the Dirichlet posteriors studied recently by Zantedeschi et al. [2021] for training voting classifiers; in contrast to that work our bounds apply to non-randomised votes via the use of margins. Our contributions add perspective to the debate on the "margins theory" proposed by Schapire et al. [1998] for the generalisation of ensemble classifiers.

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Felix Biggs, Valentina Zantedeschi, Benjamin Guedj. 2022-06-09. On Margins and Generalisation for Voting Classifiers. https://arxiv.org/abs/2206.04607

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